Variable Speed Limit Control for Highway scenarios a Multi-Agent Reinforcement Learning Based Appraoch

EasyChair Preprint 13400

Abstract

Modern road networks are critical in developing transportation infrastructures from the aspect of sustainability, thanks to the rapid increase in road users. The demand for mobility makes the existing infrastructure more crowded, boosting greenhouse gas emissions and delays in everyday commuting. Expanding the road network is only possible in some cases and is also not feasible, but Intelligent Transportation Systems (ITS) can enhance the efficiency of the existing transportation network. This paper focuses on exploiting the existing capabilities of the highway infrastructure since, in the case of sudden events such as accidents or maintenance, the highway's capacity can drop, and transients occur. These transients in the traffic can significantly increase travel time and emissions. This paper presents a Multi-Agent Reinforcement Learning-based approach to tackle this problem.

Keyphrases: Intelligent Transportation Systems, Variable Speed Limit Control, multi-agent reinforcement learning

@booklet{EasyChair:13400,
  author    = {Bálint Kővári and István Knáb and Tamás Bécsi},
  title     = {Variable Speed Limit Control for Highway scenarios a Multi-Agent Reinforcement Learning Based Appraoch},
  howpublished = {EasyChair Preprint 13400},
  year      = {EasyChair, 2024}}